Seed color assessment in rapeseed seeds using Color and Near Infrared Reflectance Spectrometers

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OŚLINY

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LEISTE

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ILSEED

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ROPS

2009

Krzysztof Michalski

Instytut Hodowli i Aklimatyzacji Roślin, Oddział w Poznaniu

Seed color assessment in rapeseed seeds using

Color and Near Infrared Reflectance Spectrometers

Ocena koloru nasion rzepaku przy użyciu spektrokolorymetru

oraz spektrometru odbiciowego na bliską podczerwień (NIRS)

Key words: rapeseed, yellow seed, color measurement, NIRS analysis

One of the basic directions in the breeding of new varieties of rapeseed is development of yellow seeded rapeseed. The advantage of yellow seeds is reduced amount of polyphenols and fiber in seeds. Yellow-seeded rapeseed contains significantly more oil and protein and less of total dietary fiber than black-seed. Visual classification method of seed color has been developed and applied in breeding works conducted in IHAR. Such examination is very unreliable and depends strongly on experience of a person performing tests, external light circumstances and other parameters, like physiological status of seeds. The Hunter Labs spectrocolorimeter (Colorflex) was applied to minimize the subjectivity of color estimations.

Transfer of visual assessment results into Colorflex readings gives satisfactory accuracy. The regression equation error is 0.4 what comprises the 0.5 minimal visually estimated differences. Calibration was done also for NIRS machine using different spectrum ranges. The calibrations using NIRS were in good agreement with Colorflex measurements and can be used as replacement of the Colorflex method. The most reliable estimations were obtained with calibration covering full spectrum (VIS +NIR). The calibration in VIS range was slightly worse. Calibration in only NIR range was giving significantly higher error, but still acceptable, what allows to apply also popular NIR spectrophotometers covering only NIR range.

Color estimation with NIRS can be added to the existing calibrations used to measure other seed components (glucosinolate, fat, protein, fiber, etc) so more complex seed examination can be achieved.

Repeatability of results is very good for Colorflex readings and acceptable for NIRS measurements. Both methods can be used for seed color assessment for breeding purposes.

Słowa kluczowe: rzepak żółtonasienny, pomiar barwy, analiza NIRS

Jednym z podstawowych kierunków hodowli nowych odmian rzepaku jest otrzymanie rzepaku żółtonasiennego. Jego zaletą jest zredukowana zawartość polifenoli i włókna w nasionach. Żółto-nasienny rzepak od dawna jest celem wielu programów hodowlanych, bowiem wykazano, że żółte nasiona zawierają więcej oleju i białka w porównaniu z rzepakiem czarnonasiennym. W IHAR do określania barwy wykorzystywano ocenę wizualną. Jej wyniki zależą od warunków takich jak doświadczenie i zdolności obserwatora, oświetlenie oraz stan fizjologiczny nasion. Aby zminima-lizować wpływ czynników subiektywnych, do pomiaru barwy nasion zastosowano spektrokolorymetr Colorflex. Na bazie wyników oznaczania wizualnego wykonano dla niego kalibrację. Za pomocą otrzymanego równania regresji określa się zabarwienie nasion z błędem standardowym 0,4 jednostki

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Krzysztof Michalski 120

bonitacyjnej przyjętej przy ocenie wizualnej, natomiast bonitacja wizualna pozwala oceniać zabar-wienie z dokładnością do 0,5 jednostki tej skali. Tak wykalibrowany aparat posłużył następnie do oznaczenia barwy próbek użytych do kalibracji spektrofotometru NIRS 6500. W wyniku kalibracji spektrofotometru otrzymano równanie regresji pozwalające oceniać kolor nasion zgodnie z wynikami pomiarów otrzymanymi za pomocą spektrokolorymetru Colorflex. Otrzymaną kalibrację NIRS można dołączyć do innych kalibracji służących do pomiaru parametrów takich jak zawartość tłuszczu, białka, glukozynolanów, różnych rodzajów włókna itp. Metoda NIRS pozwala więc na bardziej kompleksową i wielostronną ocenę materiałów hodowlanych. Powtarzalność pomiarów jest bardzo dobra dla danych otrzymanych za pomocą Colorflex i NIRS przy zastosowaniu zakresu VIS+NIR, a nieznacznie gorsza dla danych mierzonych NIRS tylko w bliskiej podczerwieni. Obie metody można używać do oceny barwy nasion w pracach hodowlanych.

Introduction

The development of yellow seeded genotypes of oilseed rape is one of the basic directions in breeding of new varieties of rapeseed. The advantage of such seeds is reduced amount of polyphenols and fibre. Yellow-seeded rapeseed has been a long-term objective for many rapeseed breeding programs, since it was shown that yellow seeds contain significantly more oil and protein and less total dietary fiber than black-seeds (Rashid et al. 1995, Piotrowska, Ochodzki 2002).

Rahman (2001) applied yellow-seeded canola to produce valuable livestock meal with high digestibility, due to reduced amounts of lignins and polyphenols. Van Denyze (1993) showed that inheritance of yellow seed color in Brassica napus is very complex due to: maternal effect, trigenic inheritance, and environmental influences. Most of the breeding programs, carried out in order to develop yellow-seeded genotypes, examined also the relationship between seed color and nutritional characteristics of seeds and meals produced thereof. The study of the inheritance of seed coat color was based on the visual determination of seed color. Van Deynze (1994) calibrated for these purposes a NIR filter analyzer equipped with 670 and 710 nm filters. The best calibration curve was based on three wavelengths (670, 2190 and 2208 nm) and had the multiple correlation coefficient value 0.987. Velasco (1996) developed a similar NIRS equation for color estimation of seeds of Indian mustard (Brassica juncea).

The visual classification method for seed color examination was developed and applied in IHAR (Ochodzki, Piotrowska 2002). Such test is rather unreliable and depends strongly on experience of the testing person, external light circumstances and other parameters, such as actual physiological status of seeds. The Hunter Labs spectrocolorimeter (Colorflex) was applied to minimize subjectivity of color estimations. This spectrocolorimeter measures the color in special system, named CIELAB, or CIEL*a*b* (Mclaren 1976, Agoston 1979). In order to arrive at the CIEL*a*b* color-space, the three colorimetric coordinates (color-values) of X, Y and Z from the CIE Standard Color Table have been

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Seed colour assessment in rapeseed seeds ... 121

transformed into the three new reference values of “L”, “a” and “b”. ”L“ value represents "psychometric brightness" (or lightness), ”a” describes color in range from green to magenta, where “b” means the range from blue to yellow. Parameter “L” is defined by the appropriate function of a psycho-physical value (a color-value) selected in such a way that uniform steps on the scale will reproduce as closely as possible the uniform differences (which are related in terms of lightness) between colors. The values of “L” extend between 0 for black and 100 for white. Parameters “L”, ”a”, “b” can be used to estimate and reproduce any natural color.

The color measurement obtained with Colorflex spectrocolorimeter gave objective results, but it was necessary to compare them with visual classification used up to now by breeders. To obtain continuity of breeding results it was necessary to check the possibility of conversion of the CIELAB data into visual assessment scale with levels “0–6”, where 0 represents regular black seeds and 6 represents pale yellow seed (Piotrowska 2002). The ColorQuest software included into a package of colorimeter allows to install regression equation which enables the conversion of measurements to visual scale. Results obtained in this way should be objective and its interpretation very easy and simple.

The work was divided into two parts: calibrating of Colorflex spectrometer to work in attendance with the former visual rating method and transfer of this calibration to the NIRS 6500 machine.

Materials and methods

Colorflex calibration

The set of 23 samples of rapeseed with different colors, from pure yellow to black, were collected and used for calibration as reference materials (Table 1). Those samples were chosen from breeding material harvested in 2007 according to differences in seed color and the uniformity of this color among seeds.

Color of every sample was evaluated visually (7 point range, where 0 responded to black and 6 responded to pale yellow) by specialist, experienced in visual examination to get the best possible color and shade estimation (Table 1).

Color of samples varied from black via brown, light brown up to pale yellow to cover the whole range of calibration. The seed samples were equilibrated to 21°C in the laboratory for 24 hr and were scanned on Colorflex spectrocolorimeter (Hunter Associates Laboratory, Inc. 11491 Sunset Hills Road) with the spectral range 400–700 nm and 10 nm resolution.

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Table 1 Comparison of visual scale and CIELAB measurements

Porównanie wyników oceny wizualnej z wynikami pomiaru w skali CIELAB

Sample no Nr próbki Visual assessment (0–5.5) Ocena wizualna L a b 1 0 21.02 1.64 1.40 2 5.5 49.56 11.91 28.49 3 2 35.53 11.78 20.95 4 3.5 39.38 12.80 27.38 5 3 38.78 11.67 24.57 6 5 47.75 12.68 29.21 7 5.5 48.76 12.97 33.49 8 5 45.23 15.87 35.06 9 5 47.00 14.23 33.76 10 5 46.10 15.88 34.81 11 3.5 38.15 14.90 28.51 12 4 39.94 14.15 29.09 13 3.5 39.94 13.16 27.99 14 4.5 42.06 13.48 30.07 15 3 37.42 13.18 26.25 16 3.5 38.63 12.56 28.26 17 3.5 38.96 13.50 27.95 18 2.5 30.47 12.47 18.41 19 2.5 26.95 10.86 13.63 20 2 28.11 11.07 14.60 21 1 23.64 8.53 8.96 22 1.5 22.31 8.10 7.94 23 1.5 27.20 11.02 14.00

NIRS Calibration

The reflectance spectra, log(1/R), of the samples were recorded on a mono-chromator (model 6500, Foss NIRSystems, Silver Springs, MD) with a spectral range of 400–2.498 nm and 2 nm wavelength increments. Diffuse reflectance readings of an internal ceramic tile within the scanning module were referenced before the sample was scanned. The samples were packed to a spinning cup cell and measured.

For the calibration purposes the set of 52 samples was collected (23 samples used for calibration of Colorflex and additional 27 samples of rapeseed selected due to color variability). All samples of this set were measured on Colorflex. The Colorflex readings were applied as reference data for NIRS calibration.

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Validation of the obtained calibration was performed with the set of 100 samples (previously measured on Colorflex spectrometer to obtain reference values). Samples were selected from yellow seeded lines bred in Poznań Department of IHAR. The seeds were harvested in 2007 and 2008. The choice was based on color shade and samples varied from light yellow to dark brown.

Results and discussion

Colorflex calibration

Test calibrations based on single parameters “L”, “a”, “b”, sum of “L, a, b” and some other arithmetic transformations were made to obtain the best transfer of visual method into Colorflex spectrocolorimeter measurements. The choice of the best transformation was done by the comparison of correlation coefficients and standard errors of regression for equation obtained for the examined transformations. The highest correlation coefficient and lowest standard deviation were obtained for the root of sum of squares “L”, “a”, “b”. Obtained estimation statistics is shown in Table 2. Figure 1 shows the relation between the visual evaluation and machine results graphically. The obtained equation y = 7.6737x+20.556 was transferred into the ColorQuest software as additional parameter (E), which presented results in scale 0–6.

E

Visual asessment — Ocena wizualna

Fig. 1. Graphical presentation of the calibration results (E represents root of sum of L, a, b squares) — Prezentacja graficzna wyników kalibracji NIRS (E oznacza pierwiastek z sumy

kwadratów L, a, b)

y = 7,6737x+20,556

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Tabl e 2 Stati stics of re gre ssi on bet w ee n vi su al cl as si fi cati o n a n d C o lo rfle x m eas ure m ents wit h t ra n sform ation = root of sum of squares L , a, b — St at yst yki regresj i po mi ędzy oce n ą w izual n ą i pom ia ra m i na Co lo rflex przy tra n sf o rma cji = p ierwi astek z sumy kw adratów L, a, b Determ inat ion c o effic ient — Ws pó łczynik determ inacji 0.92 Standard d eviation — Odch ylen ie standardowe 0.44 Sa mple s — Licz ba próbek 23.00 Slope — Na chyl enie 0.12 Intercept — Sta ła –2.20 Tabl e 3 Cali b rati on

equation statistics for

NI R + VIS (400–2500 n m ) r ange — St at yst yk a r ó w n a n ia N IR S dl a pe łn ego zakresu wi dma N IR + VIS Constituent — Sk ładnik E Numbe r of sa mpl es — Liczba próbek 52 M ean — Ś re dnia 2.290 Range — Z a kr es 0.23–5.50 M ath Tr eatm en t — Transformacja -1 , 4, 4, 1 Princ ipa l Components G łówne sk ładowe SE C standard error of ca libr ation b łą d standardowy kalibracj i RSQ correlation coeffi cien t wspó łczynnik kor elac ji F significance coeffi cien t wspó łcz ynnik istotno ści SE CV standard error of cross validation błą d standardowy walidac ji sk ro śne j 1-VR residual varian ce 1-wariancja re sztk o wa SE V

standard error of validation

b łą d standardowy walidacji BIAS SE CV bias corr ec ted

standard error of validation skorygowany błą

d walida cj i 4 0.212 0.980 16.48 0.269 0.967 0.294 0.017 0.296

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Table 4 Calib ra tio n equ atio n statistics fo r VI S (400– 110 0 n m ) r ang e — St at yst yka r ó w n a n ia dl a zakres u VI S ( 4 00 – 1 1 0 0 nm) Constituent — Sk ładnik E Numbe r of sa mpl es — Liczba próbek 52 M ean — Ś re dnia 2.342 Range — Z a kr es 0.23–5.50 M ath Tr eatm en t — Transformacja -1 , 4, 4, 1 Princ ipa l Components G łówne sk ładowe SE C standard error of ca libr ation b łą d standardowy kalibracj i RSQ correlation coeffi cien t wspó łczynnik kor elac ji F significance coeffi cien t wspó łcz ynnik istotno ści SE CV standard error o f cross valid ation b łą d standardowy walidac ji sk ro śne j 1-VR _residual varian ce 1-wariancja re sztk o wa SE V

standard error of validation skorygowany _błą

d wa lidacji 4 0.182 0.986 9.56 0. 292 0.964 0.336 0.091 0.325 Tabl e 5 Calib ratio n equ atio n statistics fo r NIR (11 0 0 –25 00 n m ) ran g e — S ta tystyka ró wnan ia d la za kresu N IR (1 10 0–2 500 nm ) Constituent — Sk ładnik E Numbe r of sa mpl es — Liczba próbek 52 M ean — Ś re dnia 2.342 Range — Z a kr es 0.23–5.50 M ath Tr eatm en t — Transformacja 1 , 4, 4, 1 Princ ipa l Components G łówne sk ładowe SE C standard error of ca libr ation b łą d standardowy kalibracj i RSQ correlation coeffi cien t wspó łczynnik kor elac ji F significance coeffi cien t wspó łcz ynnik istotno ści SE CV standard error o f cross valid ation b łą d standardowy walidac ji sk ro śne j 1-VR _residual varian ce 1-wariancja re sztk o wa SE V

standard error of validation skorygowany _błą

d walida cj i 5 0.368 0.938 22.19 0.587 0.843 0.487 0.261 0.413

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-0.337 1.247 2.831 4.415 5.998

NIR

Fig. 2. Calibration results of full range VIS-NIRS equation — Wyniki kalibracji dla równania

VIS-NIRS

0 1 2 3 4 5 6 Barwa (Predicted)

Fig. 3. Validation graph for VIS-NIR equation — Wykres walidacji równania VIS-NIR Reference 6.080 4.486 2.891 1.297 -0.297 Barwa (LAB) 6 5 4 3 2 1 0

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-0.418 1.241 2.900 4.559 6.218

NIR

Fig. 4. Calibration results of VIS range equation — Wyniki kalibracji dla równania VIS

Fig. 5. Validation graph for VIS range equation — Wykres walidacji równania w zakresie VIS Reference 6.080 4.486 2.891 1.297 -0.297 Barwa (LAB) 6 5 4 3 2 1 0

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-0.720 0.830 2.380 3.930 5.479

NIR

Fig. 6. Calibration results of NIR range equation — Wyniki kalibracji dla równania

w zakresie NIR

Fig. 7. Validation graph for NIR equation — Wykres walidacji równania w zakresie NIR Barwa (LAB) 6 5 4 3 2 1 0 Reference 6.080 4.486 2.891 1.297 -0.297

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NIRS calibration

The set of 50 samples was used as the calibration set. Calibrations obtained were validated with 100 validation samples set. As reference values the Colorflex readings transferred into scale 0–6 (E parameter) by the described equation were used. Three ranges available for NIRS machine, full range (visual and NIR 400– 2500 nm), visual only (400–1100 nm) and NIR only (1100–2500 nm) were examined. The goal of such examination was to check the compatibility with NIR machines existing on the market. Spectrometer was working in diffuse-reflectance mode. Below are presented results of obtained calibrations: full range 400–2500 nm (Table 3), visible range only 400–1100 nm (Table 4) and NIR range only 1100–2500 nm (Table 5).

To test the repeatability a set of 10 samples was measured 3 times by Colorflex and respectively by every equation for NIRS. On the basis of the obtained results the standard deviations were calculated to estimate result repeatability of each equation (Table 6).

Table 6 Results of repeatability of color estimation with the use of different methods (average, standard deviation of results; 3 repetitions) — Wyniki testu powtarzalności pomiaru koloru

nasion różnymi metodami (średnie odchylenie standardowe wyników, 3 powtórzenia)

Colorflex (reference) Vis-NIR VIS NIR

0.08 0.17 0.08 0.27

For the representation of the possibility the NIRS measurements, validation set of 100 samples was measured for simultaneous estimations of different seed constituents. Table 7 shows measurement results, for example of only 12 samples. Results of measurements were used for calculation of correlations between color and different components of seeds. It was found that significant correlation exists between seed color and important seed components (Table 8). Positive correlations were found between color and fat or protein contents but negative between color and fiber or water contents. These findings confirm the earlier findings obtained with the use of chemical analyses.

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Tabl e 7 Exam ple of si m u ltaneous measurem ent of rapesee d c o lor a n d chem ic al com posi ti on by NIR s p ec tr o p h o to m et er (i n d epe n dent eq u ation s fo r each o f th e con stitu en ts) — Pr zyk ład rów n oc zesne g o pomiaru k o lor u i sk ład u c h emi czne g o n a si on rze p a ku z a po m o cą sp ektrofo tometru b liskiej p odczerwien i (ka żdy sk ład ni k oz nac za ny ni ezal eż ny m r ó wn a n iem) GBN — glucobr assicanap in — g lukobrassicanapina [µM/g] GLB — glu cobr assicin — glu ko b ra ss ycyna [µM/g] GLN — glu conapin — g lukonapina [µM /g]

4OH — 4OH glucobrassicin

— 4OH glukobr as sycyna [µM /g] PRO — progoitr in — progoitryn a [µM /g] ALK — alkeny l total — suma a lkenowych [µM /g] NAP — napoleif erin — napoleiferyna [µM /g]

GLU — total glucosinolates —

suma glukozynolanów [µM /g] Sa mple Próbka Color Barwa GBN GLN PRO NAP GLB 4OH ALK GLU Protein Bia łko [%] Fa t T łuszc z [%] NDF [%] ADF [%] Moisture Woda [%] 1 4.73 0.44 2.56 3.24 0.12 0.24 4.18 6.71 16.25 19.61 44.39 20.96 13.10 4.42 2 4.83 0.32 0.40 1.50 0.11 0.23 3.91 2. 26 8 .99 19.29 45.90 20.49 11.96 3.97 3 4.25 0.28 0.69 1.07 0.12 0.23 3.86 1. 36 8 .03 20.53 43.78 21.19 12.69 4.35 4 3.94 0.21 1.71 0.94 0.10 0.24 3.93 2.65 11.11 19.87 49.17 20.65 11.12 4.89 5 4.49 0.46 1.80 2.37 0.13 0.25 3.95 4.75 12.63 21.10 44.01 19.09 12.41 4.64 6 4.89 0.51 1.51 2.44 0.13 0.25 4.16 3.57 12.41 21.66 41.28 19.81 10.37 4.41 7 4.02 0.37 1.54 1.37 0.11 0.23 3.90 4. 08 9 .70 20.97 46.27 21.57 10.52 5.08 8 3.88 0.23 0.52 0.11 0.12 0.22 4.10 2. 28 8 .49 19.22 43.97 22.24 12.86 4.51 9 4.34 0.23 0.57 0.22 0.11 0.26 4.23 0. 34 8 .83 19.98 46.71 20.46 11.78 4.27 10 4.47 0.33 1.89 1.02 0.11 0.22 4.07 2.95 11.58 18.65 49.86 21.11 12.19 3.26 11 4.02 0.54 1.60 2.64 0.13 0.22 4.32 3.89 10.33 20.72 46.42 18.89 11.04 4.30 12 4.50 0.32 1.56 0.29 0.11 0.24 4.37 2.15 12.00 19.01 46.55 21.76 12.11 4.30

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Table 8 Correlations between color and other rapeseed constituents (100 samples)

Korelacja pomiędzy barwą a niektórymi innymi składnikami nasion rzepaku

Cecha

Trait

Correlation coefficient

Współczynnik korelacji

Color — Barwa

Total glucosinolates — Suma glukozynolanów 0.34

Protein — Białko 0.03 Fat — Tłuszcz 0.21*

NDF -0.79** ADF -0.73**

Conclusions

On the basis of the obtained results the following conclusions can be made: 1. Transfer of visual assessment results into Colorflex readings gives satisfactory

accuracy. The regression equation error is 0.4 what comprises the 0.5 minimal visually estimated differences. Such Colorflex calibration allows inexperienced users to obtain valid and repeatable measurements and easy color differences estimation.

2. It was possible to calibrate the NIR spectrometer on the basis of results obtained by Colorflex (E). The best agreements between color estimation on Colorflex and results of NIRS measurements were obtained for the full range spectrum (visible and infrared), as shown in table 3 (validation results). 3. The calibration of NIRS machine in VIS region was slightly less accurate but

repeatability was better because this region overlap the Colorflex working range.

4. It is still possible to get acceptable results with NIRS machine for NIR range only, what allows to use popular NIR spectrometers without VIS range (e.g. NIRS 5000).

5. Color estimation can be added to the existing NIRS calibrations, what allows for more complex examination of seeds. This study demonstrated that seed color prediction can be incorporated into NIRS routine analysis with instruments that incorporate the visible spectral region.

6. The color is strongly correlated with fibre (NDF nad ADF), the other constituent shows insignificant correlations.

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